1. Field of the Invention
The present invention is related to a method of predicting residual service life for rolling bearings and a device for predicting residual service life for rolling bearings that estimates the remaining service life of rolling bearings residing on mechanical rotating devices such as pumps and fans.
2. Description of the Related Art
Rolling bearings in large numbers are found being used in the rotating components of a wide variety of machineries, and when these rolling bearings fail to work properly, various inconveniences can result, such as interruption in operation of the machinery. Generally, rolling bearings provide superb performance, and have, when used under proper conditions, long service lives, which last until repeated stress causes spalling. However, service life varies not only according to usage conditions and environment; it can even vary within identical machinery and bearings.
Mechanical problems causing stress, such as unsuitable lubricant, misalignment of the rotating shaft, or improper assembly of rolling bearings, can cause unexpected problems for rolling bearings. The majority of rolling bearing problems are caused by inadequate lubrication, the result of such factors as wear particle penetration and lubricant degradation. A variety of methods have been introduced for the purpose of predicting residual service life for these rolling bearings. Examples include methods that use accelerometers to measure vibration signals from bearings, and that sound warnings when vibration readings for the bearings exceed some activation barrier. Some methods use analysis of the frequencies of vibrations from bearings to predict the cause of defects. Some methods estimate service life by predicting the rate of increase in bearing vibration readings. Further, there are methods such as the Shock Pulse Method and the Acoustic Emission (AE) Method.
The most common prediction methods used are those that utilize predictions of the rate of increase in bearing vibration readings. These methods predict the rate of increase in vibration acceleration of bearings through the use of linear, quadratic and exponential curves, and calculate the time until the vibration readings reach a preset activation barrier.
The Shock Pulse Method is a method of prediction that utilizes shock pulses (intense compression waves) to discover rolling bearing defects in the early stages and to predict lubricant degradation levels. Generally, at the instant the rolling element (or roller) and the bearing ring of a rolling bearing come into direct contact, unique vibrations occur, and large amounts of stress focalize under the surface of the material, creating pressure waves in the material. If the contact surface has an irregular dispersion of asperities, large numbers of irregular pressure waves are created at the moment of contact. Each of these pressure waves is called an intense compression wave (or shock pulse), and dissipates as they propagate in the form of ultrasonic waves, from the point of contact throughout the rest of the bearing and the inside of the bearing housing. Here, through examination of shock pulse activity, predictions are made regarding the thickness of the lubricant film in the bearing and the degree to which the bearing has been damaged, enabling prediction of lubricant degradation levels and allowing the determination of appropriate intervals between lubricant supplementations.
The AE Method is a method of predicting residual service life that utilizes AE signals in a frequency higher than the acceleration to discover early-stage rolling bearing defects. The AE Method is a method of prediction that uses AE signals, which are created when built-up strain energy is released in the form of sound as solid objects undergo changes in shape or physical breakdown. These AE signals, which are transmissions of elastic waves, are released when elastic energy is released from inside a material, not necessarily only during physical breakdown, but also when dislocation or transformation of crystal structures in a material occurs. The AE signals are processed while the rolling bearing is in operation through the use of an AE sensor, and, by observing how often AE waves occur, predictions can be made regarding the rolling bearing.
Using these kinds of prediction methods, unanticipated rolling bearing failures can be predicted before they actually happen, and appropriate intervals for replacing affected bearings can be estimated in advance. Thus, the “normal operation life”, during which detection of irregularities in bearings occurs, and the “defective life”, during which bearing overheating and fracturing occur, can be clearly defined, and the interval between the normal operation life and defective life, in short the residual service life, can be predicted. For mechanical rotating devices, predictions regarding whether there are irregularities and regarding what the cause of the irregularities are can be carried out, and the extent of any irregularities can be predicted, enabling determination of appropriate maintenance intervals for rolling bearings.
The most common kinds of statistical prediction for vibration acceleration, utilize, as a parameter, readings of vibrations until fulfillment of the predicted service life, and carry out curvilinear regression using quadratic and exponential curves so as to define residual service life as the period until vibration readings reach some activation barrier. Further, with the Shock Pulse Method, the frequency of shock pulses can be used to predict lubricant degradation levels, enabling the determination of appropriate intervals for lubricant supplementation. Alternately, the AE Method predicts residual service life in the same way as statistical predictions that use vibration acceleration.
However, with the traditional prediction methods described above, setting the activation barrier for vibrations is difficult, and residual service life varies greatly depending on the activation barrier; thus, making accurate predictions of residual service life is extremely difficult. Further, by the time vibrations begin increasing, the rolling bearing is already in the stage of total fatigue failure, making the creation of long-term maintenance plans difficult; moreover, even when prediction of residual service life is carried out, repairs may not be implemented in time. As a result, in practice, rolling bearings are often replaced in the early stages, despite the fact that they may not yet be close to the end of their true service life. Further, because of the poor precision in predicting residual service life for these methods, at actual power plants, factories and similar facilities, it is impossible to extend the interval between inspection cycles, necessitating maintenance systems where every single bearing is replaced during set inspection times every few years. The inability of these methods to reduce costs and labor—which is the main purpose of maintenance—is thus a problem.
Again, although the said traditional Shock Pulse Method can detect lubricant degradation in the early stages and determine appropriate intervals for lubricant supplementation, it still has the limitation of not being able to make precise predictions regarding residual service life based on the current conditions.
Further, although the traditional AE Method can predict residual service life at an earlier stage than the said statistical methods using vibration acceleration, the AE (Acoustic Emission) sensor and the signal processing circuit that are used for predictions are cost-prohibitive; in addition, AE waves are very faint, making the method prone to noise interference.
The present invention was devised to solve the aforementioned problems. In short, the object of the present invention is to provide a method of predicting residual service life for rolling bearings and a device for predicting residual service life for rolling bearings that can, in a cost-effective manner, utilize signals in resonant frequency bands or signals in high frequency bands obtained via an accelerometer in order to detect wear particle penetration in the lubricant, as well as lubricant degradation, both of which greatly affect rolling bearing service life, and that can in the early stages, using the wear particle penetration and lubricant degradation as a basis, accurately estimate service life for rolling bearings.